Multivariate Statistical Analysis in Neuroscience

Research Paper (postgraduate) from the year 2015 in the subject Medicine - Other grade: II Level Master University of Pavia (Unit of Medical and Genomic Statistics) course: Medical and Genomic Statistics language: English abstract: Electroencephalography commonly called 'EEG' estimates through the application of electrodes the electrical activity of the brain (which is the sum of the electrical activity of each neuron). In recent years with the goal of making more reliable the EEG many researchers have turned their interest in the development of tools methods and software. This thesis describes some best procedures for the experimental design data visualization and descriptive or inferential statistical analysis. The application of statistical models to single or multiple subjects study-design are also described including parametric and non-parametric approaches. Methods for processing multivariate data (PCA ICA clustering) were described. Re-sampling methods (bootstrap) using many randomly software-generated samples were also described. The aim of this work is to provide with statistical concepts and examples information on the qualitative and quantitative approaches related to the electroencephalographic signals. The work consists into three parts: INTRODUTION TO ELECTROENCEPHALOGRAPHY (GENERAL CHARACTERISTICS); DATA MINING AND STATISTICAL ANALYSIS; EXPERIMENTAL STUDY DESIGNS. The six works included in the section called EXPERIMENTAL STUDY DESIGNS analyze EEG alterations in the protocols: Electrocortical activity in dancers and non-dancers listening to different music genre and during imaginative dance motor activity; Electrocortical activity during monosynaptic reflex in athletes; Monitoring of electrocortical activity for evaluation of seasickness; Electrocortical activity in different body positions; Electrocortical activity in athletes and non-athletes during body balance tasks; Electrocortical responses in volunteers with and without specif